Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing

WANG Aichun; XIANG Maosheng; WANG Bingnan

doi:10.11999/JEIT160222

Volume 39 Issue 1

Jan. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(1): 95-102

WANG Aichun, XIANG Maosheng, WANG Bingnan. Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2017, 39(1): 95-102. doi: 10.11999/JEIT160222

Citation:

WANG Aichun, XIANG Maosheng, WANG Bingnan. Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2017, 39(1): 95-102. doi: 10.11999/JEIT160222

Citation:

PDF( 8136 KB)

Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing

doi: 10.11999/JEIT160222

1.
2.
(National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Development and Reform Commission Satellite and Application Development Projects of China [2012] 2083

Received Date: 2016-03-07
Rev Recd Date: 2016-07-18
Publish Date: 2017-01-19

Abstract

Abstract

While the use of differential SAR tomography based on Compressive Sensing (CS) makes it possible to reconstruct the four-dimensional information of an observed scene, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, a method using differential SAR tomography based on Khatri-Rao Subspace and Block Compressive Sensing (KRS-BCS) is proposed. Using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix, the proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao Subspace, and then the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CS methods, the proposed KRS-BCS method not only maintains the high resolution characteristics of CS methods, but also has higher reconstruction accuracy and better performance. Simulations, ENVISAT-ASAR data and ground-based GPS data verify the effectiveness of the proposed method.
- Differential SAR tomography imaging,
- Khatri-Rao Subspace (KRS),
- Block Compressive Sensing (BCS)

FullText(HTML)

References(30)

References

LOMBARDINI F. Differential tomography: a new framework for SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(1): 37-44. doi: 10.1109/ TGRS.2004.838371.

SERAFINO F, SOLDOVIERI F, LOMBARDINI F, et al. Singular value decomposition applied to 4D SAR imaging[C]. International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005: 2701-2704.

任笑真, 杨汝良. 一种基于逆问题的差分干涉SAR层析成像方法[J]. 电子与信息学报, 2010, 32(3): 582-586. doi: 10.3724 /SP. J.1146.2009.00259.

REN Xiaozhen and YANG Ruliang. An inverse problem based approach for differential SAR tomography imaging[J]. Journal of Electronics Information Technology, 2010, 32(3): 582-586. doi: 10.3724/SP.J.1146.2009.00259.

孙希龙, 余安喜, 董臻, 等.一种差分高分辨率成像方法[J].电子与信息学报, 2012, 34 (2): 273-278. doi: 10.3724/SP.J.1146. 2011.00676.

SUN Xilong, YU Anxi, DONG Zhen, et al. A high resolution imaging method for differential SAR tomography[J]. Journal of Electronics Information Technology, 2012, 34(2): 273-278. doi: 10.3724/SP.J.1146.2011.00676.

吴一戎, 洪文, 张冰尘, 等. 稀疏微波成像研究进展[J]. 雷达学报, 2014, 3(4): 384-395. doi: 10.3724/SP.J.1300.2014. 14105.

WU Yirong, HONG Wen, ZHANG Bingchen, et al. Current development of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 384-395. doi: 10.3724/SP.J.1300.2014. 14105.

李少东, 杨军, 陈文峰, 等. 基于压缩感知理论的雷达成像技术与应用研究进展[J]. 电子与信息学报, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874.

LI Shaodong, YANG Jun, CHEN Wenfeng, et al. Overview of radar imaging technique and application based on compressive sensing theory[J]. Journal of Electronics Information Technology, 2016, 38(2): 495-508. doi: 10.11999/ JEIT150874.

ZHU X X and BAMLER R. Lets do the time warp: multicomponent nonlinear motion estimation in differential SAR tomography[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 735-739. doi: 10.1109/LGRS.2010. 2013298.

REN X Z, LI Y F, and YANG R L. Four-dimensional SAR imaging scheme based on compressive sensing[J]. Progress in Electromagnetics Research B, 2012, 39(39): 225-239. doi: 10.2528/PIERB11121212.

ZHU X X and BAMLER R. Supperresolving SAR tomography for multidimensional imaging of urban areas: compressive sensing-based TomoSAR inversion[J]. IEEE Signal and Processing Magazine, 2014, 31(4): 51-58. doi: 10.1109/MSP.2014.2312098.

WANG Y Y, ZHU X X, and BAMLER R. An efficient tomographic inversion approach for urban mapping using meter resolution SAR image stacks[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1250-1254. doi: 10.1109 /LGRS.2013.2290833.

SIDDIQUE M A, HAJNSEK I, WEGMULLER U, et al. Investigating the combined use of differential SAR tomography and PSI for spatio-temporal inversion[C]. Urban Remote Sensing Event (JURSE), Lausanne, Switzerland, 2015: 1-4.

DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.

ZHU X X and BAMLER R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 247-258. doi: 10.1109/TGRS.2011.2160183.

李烈辰, 李道京. 基于压缩感知的连续场景稀疏阵列SAR三维成像[J]. 电子与信息学报, 2014, 36(9): 2166-2172. doi: 10.3724/SP.J.1146.2013.01645.

LI Liechen and LI Daojing. Sparse array 3D imaging for continuous scene based on compressed sensing[J]. Journal of Electronics Information Technology, 2014, 36(9): 2166-2172. doi: 10.3724/SP.J.1146.2013.01645.

张冰尘, 王万影, 毕辉, 等. 基于压缩多信号分类算法的森林区域极化SAR层析成像[J]. 电子与信息学报, 2015, 37(3): 625-630. doi: 10.11999/JEIT140584.

ZHANG Bingchen, WANG Wanying, BI Hui, et al. Polarimetric SAR tomography for forested areas based on compressive multiple signal classification[J]. Journal of Electronics Information Technology, 2015, 37(3): 625-630. doi: 10.11999/JEIT140584.

廖明生, 魏恋欢, 汪紫芸, 等. 压缩感知在城区高分辨率SAR层析成像中的应用[J]. 雷达学报, 2015, 4(2): 124-129. doi: 10.12000/JR15031.

LIAO Mingsheng, WEI Lianhuan, WANG Ziyun, et al. Compressive sensing in high-resolution 3D SAR tomography of urban scenarios[J]. Journal of Radars, 2015, 4(2): 124-129. doi: 10.12000/JR15031.

王爱春, 向茂生. 基于块压缩感知的SAR层析成像方法[J]. 雷达学报, 2016, 5(1): 57-64. doi: 10.12000/JR16006.

WANG Aichun and XIANG Maosheng. SAR tomography based on block compressive sensing[J]. Journal of Radars, 2016, 5(1): 157-64. doi: 10.12000/JR16006.

ELDAR Y C, KUPPINGER P, and BOLCSKEI H. Block- sparse signals: Uncertainty relations and efficient recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054. doi: 10.1109/TSP.2010.2044837.

李廉林, 周小阳, 崔铁军. 结构化信号处理理论和方法的研究进展[J]. 雷达学报, 2015, 4(5): 491-502. doi: 10.12000/ JR15111.

LI Lianlin, ZHOU Xiaoyang, and CUI Tiejun. Perspectives on theories and methods of structural signal processing[J]. Journal of Radars, 2015, 4(5): 491-502. doi: 10.12000/ JR15111.

SHERVASHIDZE N and BACH F. Learning the structure for structured sparsity[J]. IEEE Transactions on Signal Processing, 2015, 63(18): 4894-4902. doi: 10.1109/TSP.2015. 2446432.

TERADA T, NISHIMURA T, OGAWA Y, et al. DOA estimation for multi-band signal sources using compressed sensing techniques with Kratri-Rao processing[J]. IEITC Transactions on Communications, 2014, 97(10): 2110-2117. doi: 10.1587/transcom.E97.B.2110.

Relative Articles

Supplements(0)

Cited By

Proportional views